Understanding the Relationship Between Changes in Accessibility to Jobs, Income, and Unemployment in Toronto, Canada
Bibliographic record
Abstract
In many cities, transport investments are being directed towards increasing access in socially deprived neighbourhoods in order to enhance quality of life and improve equity.However, little research has been conducted to assess the impacts of such targeted interventions on the well-being of these individuals and the resulting equity of outcome.This study aims to evaluate the impacts of accessibility improvements overtime on neighbourhood socio-economic status, by examining the relationship between changes in accessibility to employment opportunities by public transport and changes in income and unemployment in the Greater Toronto and Hamilton Area, Canada (GTHA).To investigate this relationship, two linear regression models are proposed in our study.The results show that accessibility to jobs by public transport is vertically equitable in the GTHA (i.e., low-income neighborhoods experience higher levels of accessibility), although vertical equity decreased during the study period.The regression models suggest that, for low and medium income census tracts, transit accessibility improvements are associated with increases in median household income and decreases in the unemployment rate, whilst controlling for local migration.For high-income census tracts, increases in accessibility by public transport are related to decreases in income, potentially due to the migration of high-income populations to less dense neighbourhoods, away from transit.The relationship uncovered in this study highlights the impacts of accessibility improvements on low and medium income areas.The findings from our study provide a case for transport engineers, planners, and policy makers regarding the importance of positive changes in accessibility as a tool to derive equity outcomes in low income areas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".